This site is built with fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks.

fastpages automates the process of creating blog posts via GitHub Actions, so you don’t have to fuss with conversion scripts. A full list of features can be found on GitHub.

# Posts

In this post, we will implement the variational AutoEncoder (VAE) for an image dataset of celebrity faces. This is the Programming Assignment of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 14, 2021

In this post, we will cover the easy way to handle KL divergence with tensorflow probability layer object. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 14, 2021

In this post, we will cover the complete implementation of Variational AutoEncoder, which can optimize the ELBO objective function. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 14, 2021

In this post, we will see how the KL divergence can be computed between two distribution objects, in cases where an analytical expression for the KL divergence is known. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 13, 2021

In this post, we will implement simple autoencoder architecture. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 13, 2021

In this post, we are take a look at an application for RealNVP. This is a homework assignment of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 8, 2021

In this post, we are going to take a look at Autoregressive flows and RealNVP. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 8, 2021

In this post, we are going to make customized transformation with our own bijectors for fexibility. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 7, 2021

In this post, we are going to take a look at transform distribution objects as a module. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Sep 7, 2021

In this post, we are going to take a look at bijectors which are the objects intense flow probability that implemented by bijective or invertible transformations. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 30, 2021

In this post, we will create a Bayesian convolutional neural network to classify the famous MNIST handwritten digits. This will be a probabilistic model, designed to capture both aleatoric and epistemic uncertainty. You will test the uncertainty quantifications against a corrupted version of the dataset. This is the assignment of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 26, 2021

In this post, we will cover how to use DenseReparameterization layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 24, 2021

In this post, we will cover prior distribution over the weight and obtain posterior distribution. We will implement feed-forward network using the DenseVariational Layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 24, 2021

In this post, we will introduce other probabilistic layers and how we can use them.. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 23, 2021

In this post, we will introduce the most direct way of incorporating distribution object into a deep learning model with distribution lambda layer. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.

Aug 19, 2021